December 2011
Intermediate to advanced
243 pages
8h 52m
English
book
Statistical Learning and Data Science
by Mireille Gettler Summa, Leon Bottou, Bernard Goldfarb, Fionn Murtagh, Catherine Pardoux, Myriam Touati
Content preview from Statistical Learning and Data Science
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12
Bayesian Analysis of Structural Equation Models Using
Parameter Expansion
Séverine Deme yer, Jean-Louis Foulley, Nicolas Fischer, and Gilbert Saporta
CONTENTS
12.1 Introduction . . . . . .. . .. . . . . .. . .. . . . . .. . .. . . . . .. . . .. . .. . . . . .. . .. . . . . .. . .. . . .. .. . . . 135
12.1.1 From Latent Variables to Structural Equation Models (SEM) . .. . . .. 13 5
12.1.2 Motivation of a Bayesian Approach to SEM . . . .. . .. . .. . .. . .. . .. . .. . . . 136
12.1.3 Motivation of a Parameter Expansion (PX) Framework . . .. . . .. . .. . .. 137
12.2 Specification of SEM for Mixed Observed Variables . . . .. . .. . .. . .. . .. . .
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